OpenRaiser/Intern-Atlas huggingface dataset:
Robust coherence-based spectral enhancement for distant speech recognition
Venue: arXiv cs.SD 2015
Year: 2015

In this contribution to the 3rd CHiME Speech Separation and Recognition Challenge (CHiME-3) we extend the acoustic front-end of the CHiME-3 baseline speech recognition system by a coherence-based Wiener filter which is applied to the output signal of the baseline beamformer. To compute the time- and frequency-dependent postfilter gains the ratio between direct and diffuse signal components at the output of the baseline beamformer is estimated and used as approximation of the short-time signal-to-noise ratio. The proposed spectral enhancement technique is evaluated with respect to word error rates of the CHiME-3 challenge baseline speech recognition system using real speech recorded in public environments. Results confirm the effectiveness of the coherence-based postfilter when integrated into the front-end signal enhancement.
Boolean Hedonic Games
Venue: arXiv cs.GT 2015
Year: 2015

We study hedonic games with dichotomous preferences. Hedonic games are cooperative games in which players desire to form coalitions, but only care about the makeup of the coalitions of which they are members; they are indifferent about the makeup of other coalitions. The assumption of dichotomous preferences means that, additionally, each player's preference relation partitions the set of coalitions of which that player is a member into just two equivalence classes: satisfactory and unsatisfactory. A player is indifferent between satisfactory coalitions, and is indifferent between unsatisfactory coalitions, but strictly prefers any satisfactory coalition over any unsatisfactory coalition. We develop a succinct representation for such games, in which each player's preference relation is represented by a propositional formula. We show how solution concepts for hedonic games with dichotomous preferences are characterised by propositional formulas.
Graphic Realizations of Joint-Degree Matrices
Venue: arXiv cs.DM 2015
Year: 2015

In this paper we introduce extensions and modifications of the classical degree sequence graphic realization problem studied by Erdős-Gallai and Havel-Hakimi, as well as of the corresponding connected graphic realization version. We define the joint-degree matrix graphic (resp. connected graphic) realization problem, where in addition to the degree sequence, the exact number of desired edges between vertices of different degree classes is also specified. We give necessary and sufficient conditions, and polynomial time decision and construction algorithms for the graphic and connected graphic realization problems. These problems arise naturally in the current topic of graph modeling for complex networks. From the technical point of view, the joint-degree matrix realization algorithm is straightforward. However, the connected joint-degree matrix realization algorithm involves a novel recursive search of suitable local graph modifications. Also, we outline directions for further work of both theoretical and practical interest. In particular, we give a Markov chain which converges to the uniform distribution over all realizations. We show that the underline state space is connected, and we leave the question of the mixing rate open.
Well Tops Guided Prediction of Reservoir Properties using Modular Neural Network Concept A Case Study from Western Onshore, India
Venue: arXiv cs.NE 2015
Year: 2015

This paper proposes a complete framework consisting pre-processing, modeling, and post-processing stages to carry out well tops guided prediction of a reservoir property (sand fraction) from three seismic attributes (seismic impedance, instantaneous amplitude, and instantaneous frequency) using the concept of modular artificial neural network (MANN). The data set used in this study comprising three seismic attributes and well log data from eight wells, is acquired from a western onshore hydrocarbon field of India. Firstly, the acquired data set is integrated and normalized. Then, well log analysis and segmentation of the total depth range into three different units (zones) separated by well tops are carried out. Secondly, three different networks are trained corresponding to three different zones using combined data set of seven wells and then trained networks are validated using the remaining test well. The target property of the test well is predicted using three different tuned networks corresponding to three zones; and then the estimated values obtained from three different networks are concatenated to represent the predicted log along the complete depth range of the testing well. The application of multiple simpler networks instead of a single one improves the prediction accuracy in terms of performance metrics such as correlation coefficient, root mean square error, absolute error mean and program execution time.
Analysis of Short Blocklength Codes for Secrecy
Venue: arXiv cs.CR 2015
Year: 2015

In this paper we provide secrecy metrics applicable to physical-layer coding techniques with finite blocklengths over Gaussian and fading wiretap channel models. Our metrics go beyond some of the known practical secrecy measures, such as bit error rate and security gap, so as to make lower bound probabilistic guarantees on error rates over short blocklengths both preceding and following a secrecy decoder. Our techniques are especially useful in cases where application of traditional information-theoretic security measures is either impractical or simply not yet understood. The metrics can aid both practical system analysis, and practical system design for physical-layer security codes. Furthermore, these new measures fill a void in the current landscape of practical security measures for physical-layer security coding, and may assist in the wide-scale adoption of physical-layer techniques for security in real-world systems. We also show how the new metrics provide techniques for reducing realistic channel models to simpler discrete memoryless wiretap channel equivalents over which existing secrecy code designs may achieve information-theoretic security.
A review of learning vector quantization classifiers
Venue: arXiv cs.LG 2015
Year: 2015

In this work we present a review of the state of the art of Learning Vector Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to date. The main concepts associated with modern LVQ approaches are defined. A comparison is made among eleven LVQ classifiers using one real-world and two artificial datasets.
Negotiation Games
Venue: arXiv cs.LO 2015
Year: 2015

Negotiations, a model of concurrency with multi party negotiation as primitive, have been recently introduced by J. Desel and J. Esparza. We initiate the study of games for this model. We study coalition problems: can a given coalition of agents force that a negotiation terminates (resp. block the negotiation so that it goes on forever)?; can the coalition force a given outcome of the negotiation? We show that for arbitrary negotiations the problems are EXPTIME-complete. Then we show that for sound and deterministic or even weakly deterministic negotiations the problems can be solved in PTIME. Notice that the input of the problems is a negotiation, which can be exponentially more compact than its state space.
Impulse Noise and Narrowband PLC
Venue: arXiv cs.IT 2015
Year: 2015

We discuss the influence of random- and periodic impulse noise on narrowband (< 500 kHz frequency band) Power Line Communications. We start with random impulse noise and compare the properties of the measured impulse noise with the common theoretical models like Middleton Class-A and Mixed Gaussian. The main difference is the fact that the measured impulse noise is noise with memory for the narrowband communication, whereas the theoretical models are memoryless. Since the FFT can be seen as a randomizing, operation, the impulse noise is assumed to appear as Gaussian noise after the FFT operation with a variance that is determined by the energy of the impulses. We investigate the problem of capacity loss due to this FFT operation. Another topic is that of periodical noise. Since periodic in the time domain means periodic in the frequency domain, this type of noise directly influences the output of the FFT for an OFDM based transmission. Randomization is necessary to avoid bursty- or dependent errors.
Charging Games in Networks of Electrical Vehicles
Venue: arXiv cs.GT 2015
Year: 2015

In this paper, a static non-cooperative game formulation of the problem of distributed charging in electrical vehicle (EV) networks is proposed. This formulation allows one to model the interaction between several EV which are connected to a common residential distribution transformer. Each EV aims at choosing the time at which it starts charging its battery in order to minimize an individual cost which is mainly related to the total power delivered by the transformer, the location of the time interval over which the charging operation is performed, and the charging duration needed for the considered EV to have its battery fully recharged. As individual cost functions are assumed to be memoryless, it is possible to show that the game of interest is always an ordinal potential game. More precisely, both an atomic and nonatomic versions of the charging game are considered. In both cases, equilibrium analysis is conducted. In particular, important issues such as equilibrium uniqueness and efficiency are tackled. Interestingly, both analytical and numerical results show that the efficiency loss due to decentralization (e.g., when cost functions such as distribution network Joule losses or life of residential distribution transformers when no thermal inertia is assumed) induced by charging is small and the corresponding "efficiency", a notion close to the Price of Anarchy, tends to one when the number of EV increases.
Node Dominance: Revealing Community and Core-Periphery Structure in Social Networks
Venue: arXiv cs.SI 2015
Year: 2015

This study relates the local property of node dominance to local and global properties of a network. Iterative removal of dominated nodes yields a distributed algorithm for computing a core-periphery decomposition of a social network, where nodes in the network core are seen to be essential in terms of network flow and global structure. Additionally, the connected components in the periphery give information about the community structure of the network, aiding in community detection. A number of explicit results are derived, relating the core and periphery to network flow, community structure and global network structure, which are corroborated by observational results. The method is illustrated using a real world network (DBLP co-authorship network), with ground-truth communities.
Cooperative Equilibrium beyond Social Dilemmas: Pareto Solvable Games
Venue: arXiv cs.GT 2015
Year: 2015

A recently introduced concept of "cooperative equilibrium", based on the assumption that players have a natural attitude to cooperation, has been proven a powerful tool in predicting human behaviour in social dilemmas. In this paper, we extend this idea to more general game models, termed "Pareto solvable" games, which in particular include the Nash Bargaining Problem and the Ultimatum Game. We show that games in this class possess a unique pure cooperative equilibrium. Furthermore, for the Ultimatum Game, this notion appears to be strongly correlated with a suitably defined variant of the Dictator Game. We support this observation with the results of a behavioural experiment conducted using Amazon Mechanical Turk, which demonstrates that our approach allows for making statistically precise predictions of average behaviour in such settings.
Incremental Loop Closure Verification by Guided Sampling
Venue: arXiv cs.CV 2015
Year: 2015

Loop closure detection, the task of identifying locations revisited by a robot in a sequence of odometry and perceptual observations, is typically formulated as a combination of two subtasks: (1) bag-of-words image retrieval and (2) post-verification using RANSAC geometric verification. The main contribution of this study is the proposal of a novel post-verification framework that achieves good precision recall trade-off in loop closure detection. This study is motivated by the fact that not all loop closure hypotheses are equally plausible (e.g., owing to mutual consistency between loop closure constraints) and that if we have evidence that one hypothesis is more plausible than the others, then it should be verified more frequently. We demonstrate that the problem of loop closure detection can be viewed as an instance of a multi-model hypothesize-and-verify framework and build guided sampling strategies on the framework where loop closures proposed using image retrieval are verified in a planned order (rather than in a conventional uniform order) to operate in a constant time. Experimental results using a stereo SLAM system confirm that the proposed strategy, the use of loop closure constraints and robot trajectory hypotheses as a guide, achieves promising results despite the fact that there exists a significant number of false positive constraints and hypotheses.
Discriminative Map Retrieval Using View-Dependent Map Descriptor
Venue: arXiv cs.CV 2015
Year: 2015

Map retrieval, the problem of similarity search over a large collection of 2D pointset maps previously built by mobile robots, is crucial for autonomous navigation in indoor and outdoor environments. Bag-of-words (BoW) methods constitute a popular approach to map retrieval; however, these methods have extremely limited descriptive ability because they ignore the spatial layout information of the local features. The main contribution of this paper is an extension of the bag-of-words map retrieval method to enable the use of spatial information from local features. Our strategy is to explicitly model a unique viewpoint of an input local map; the pose of the local feature is defined with respect to this unique viewpoint, and can be viewed as an additional invariant feature for discriminative map retrieval. Specifically, we wish to determine a unique viewpoint that is invariant to moving objects, clutter, occlusions, and actual viewpoints. Hence, we perform scene parsing to analyze the scene structure, and consider the "center" of the scene structure to be the unique viewpoint. Our scene parsing is based on a Manhattan world grammar that imposes a quasi-Manhattan world constraint to enable the robust detection of a scene structure that is invariant to clutter and moving objects. Experimental results using the publicly available radish dataset validate the efficacy of the proposed approach.
A Cyberinfrastructure-based Approach to Real Time Water Temperature Prediction
Venue: arXiv cs.DC 2015
Year: 2015

The prediction of water temperature is crucial for aquatic ecosystem studies and management. In this paper, we raise challenging issues in supporting real time water temperature prediction and present a system called WT-Agabus to address those issues. The WT-Agabus system is designed to be a cyberinfrastructure and to support various prediction models in a uniform way. In addition, we present a neural network-based water temperature prediction model to use only data available online from Korea Meteorological Administration (KMA). In this paper, we also show the current prototype implementation of the WT-Agabus system to support the prediction model
Self-localization Using Visual Experience Across Domains
Venue: arXiv cs.CV 2015
Year: 2015

In this study, we aim to solve the single-view robot self-localization problem by using visual experience across domains. Although the bag-of-words method constitutes a popular approach to single-view localization, it fails badly when it's visual vocabulary is learned and tested in different domains. Further, we are interested in using a cross-domain setting, in which the visual vocabulary is learned in different seasons and routes from the input query/database scenes. Our strategy is to mine a cross-domain visual experience, a library of raw visual images collected in different domains, to discover the relevant visual patterns that effectively explain the input scene, and use them for scene retrieval. In particular, we show that the appearance and the pose of the mined visual patterns of a query scene can be efficiently and discriminatively matched against those of the database scenes by employing image-to-class distance and spatial pyramid matching. Experimental results obtained using a novel cross-domain dataset show that our system achieves promising results despite our visual vocabulary being learned and tested in different domains.
Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we evaluate convolutional neural network (CNN) features using the AlexNet architecture and very deep convolutional network (VGGNet) architecture. To date, most CNN researchers have employed the last layers before output, which were extracted from the fully connected feature layers. However, since it is unlikely that feature representation effectiveness is dependent on the problem, this study evaluates additional convolutional layers that are adjacent to fully connected layers, in addition to executing simple tuning for feature concatenation (e.g., layer 3 + layer 5 + layer 7) and transformation, using tools such as principal component analysis. In our experiments, we carried out detection and classification tasks using the Caltech 101 and Daimler Pedestrian Benchmark Datasets.
Properties of Farey Sequence and their Applications to Digital Image Processing
Venue: arXiv cs.OH 2015
Year: 2015

Farey sequence has been a topic of interest to the mathematicians since the very beginning of last century. With the emergence of various algorithms involving the digital plane in recent times, several interesting works related with the Farey sequence have come up. Our work is related with the problem of searching an arbitrary fraction in a Farey sequence and its relevance to image processing. Given an arbitrary fraction p/q (0 < p < q) and a Farey sequence Fn of order n, we propose a novel algorithm using the Regula Falsi method and the concept of Farey table to efficiently find the fraction of Fn closest to p/q. All computations are in the integer domain only, which is its added benefit. Some contemporary applications of image processing have also been shown where such concepts can be incorporated. Experimental results have been furnished to demonstrate its efficiency and elegance.
The Design and Implementation of the Wave Transactional Filesystem
Venue: arXiv cs.DC 2015
Year: 2015

This paper introduces the Wave Transactional Filesystem (WTF), a novel, transactional, POSIX-compatible filesystem based on a new file slicing API that enables efficient file transformations. WTF provides transactional access to a distributed filesystem, eliminating the possibility of inconsistencies across multiple files. Further, the file slicing API enables applications to construct files from the contents of other files without having to rewrite or relocate data. Combined, these enable a new class of high-performance applications. Experiments show that WTF can qualitatively outperform the industry-standard HDFS distributed filesystem, up to a factor of four in a sorting benchmark, by reducing I/O costs. Microbenchmarks indicate that the new features of WTF impose only a modest overhead on top of the POSIX-compatible API.
Learning-Based Modular Indirect Adaptive Control for a Class of Nonlinear Systems
Venue: arXiv cs.SY 2015
Year: 2015

We study in this paper the problem of adaptive trajectory tracking control for a class of nonlinear systems with parametric uncertainties. We propose to use a modular approach, where we first design a robust nonlinear state feedback which renders the closed loop input-to-state stable (ISS), where the input is considered to be the estimation error of the uncertain parameters, and the state is considered to be the closed-loop output tracking error. Next, we augment this robust ISS controller with a model-free learning algorithm to estimate the model uncertainties. We implement this method with two different learning approaches. The first one is a model-free multi-parametric extremum seeking (MES) method and the second is a Bayesian optimization-based method called Gaussian Process Upper Confidence Bound (GP-UCB). The combination of the ISS feedback and the learning algorithms gives a learning-based modular indirect adaptive controller. We show the efficiency of this approach on a two-link robot manipulator example.
Algorithms for Linear Bandits on Polyhedral Sets
Venue: arXiv cs.LG 2015
Year: 2015

We study stochastic linear optimization problem with bandit feedback. The set of arms take values in an $N$-dimensional space and belong to a bounded polyhedron described by finitely many linear inequalities. We provide a lower bound for the expected regret that scales as $Ω(N\log T)$. We then provide a nearly optimal algorithm and show that its expected regret scales as $O(N\log^{1+ε}(T))$ for an arbitrary small $ε>0$. The algorithm alternates between exploration and exploitation intervals sequentially where deterministic set of arms are played in the exploration intervals and greedily selected arm is played in the exploitation intervals. We also develop an algorithm that achieves the optimal regret when sub-Gaussianity parameter of the noise term is known. Our key insight is that for a polyhedron the optimal arm is robust to small perturbations in the reward function. Consequently, a greedily selected arm is guaranteed to be optimal when the estimation error falls below some suitable threshold. Our solution resolves a question posed by Rusmevichientong and Tsitsiklis (2011) that left open the possibility of efficient algorithms with asymptotic logarithmic regret bounds. We also show that the regret upper bounds hold with probability $1$. Our numerical investigations show that while theoretical results are asymptotic the performance of our algorithms compares favorably to state-of-the-art algorithms in finite time as well.
A new result of the scaling law of weighted L1 minimization
Venue: arXiv cs.IT 2015
Year: 2015

This paper study recovery conditions of weighted L1 minimization for signal reconstruction from compressed sensing measurements. A sufficient condition for exact recovery by using the general weighted L1 minimization is derived, which builds a direct relationship between the weights and the recoverability. Simulation results indicates that this sufficient condition provides a precise prediction of the scaling law for the weighted L1 minimization.
Error Gradient-based Variable-Lp Norm Constraint LMS Algorithm for Sparse System Identification
Venue: arXiv cs.SY 2015
Year: 2015

Sparse adaptive filtering has gained much attention due to its wide applicability in the field of signal processing. Among the main algorithm families, sparse norm constraint adaptive filters develop rapidly in recent years. However, when applied for system identification, most priori work in sparse norm constraint adaptive filtering suffers from the difficulty of adaptability to the sparsity of the systems to be identified. To address this problem, we propose a novel variable p-norm constraint least mean square (LMS) algorithm, which serves as a variant of the conventional Lp-LMS algorithm established for sparse system identification. The parameter p is iteratively adjusted by the gradient descent method applied to the instantaneous square error. Numerical simulations show that this new approach achieves better performance than the traditional Lp-LMS and LMS algorithms in terms of steady-state error and convergence rate.